|
In the GIFs below, Modin (left) and pandas (right) perform *the same pandas operations* on a 2GB dataset. The only difference between the two notebook examples is the import statement.
 |
 |
|---|---|
 |
 |
### Installation
#### From PyPI
Modin can be installed with `pip` on Linux, Windows and MacOS:
```bash
pip install modin[all] # (Recommended) Install Modin with all of Modin's currently supported engines.
```
If you want to install Modin with a specific engine, we recommend:
```bash
pip install modin[ray] # Install Modin dependencies and Ray.
pip install modin[dask] # Install Modin dependencies and Dask.
pip install modin[unidist] # Install Modin dependencies and Unidist to run on Unidist
```
Modin automatically detects which engine(s) you have installed and uses that for scheduling computation.
#### From conda-forge
Installing from [conda forge](https://github.com/conda-forge/modin-feedstock) using `modin-all`
will install Modin and four engines: [Ray](https://github.com/ray-project/ray), [Dask](https://github.com/dask/dask),
[Unidist](https://github.com/modin-project/unidist) and [HDK](https://github.com/intel-ai/hdk).
```bash
conda install -c conda-forge modin-all
```
Each engine can also be installed individually (and also as a combination of several engines):
```bash
conda install -c conda-forge modin-ray # Install Modin dependencies and Ray.
conda install -c conda-forge modin-dask # Install Modin dependencies and Dask.
conda install -c conda-forge modin-unidist # Install Modin dependencies and Unidist.
conda install -c conda-forge modin-hdk # Install Modin dependencies and HDK.
```
To speed up conda installation we recommend using libmamba solver. To do this install it in a base environment:
```bash
conda install -n base conda-libmamba-solver
```
and then use it during istallation either like:
```bash
conda install -c conda-forge modin-ray modin-hdk --experimental-solver=libmamba
```
or starting from conda 22.11 and libmamba solver 22.12 versions:
```bash
conda install -c conda-forge modin-ray modin-hdk --solver=libmamba
```
#### Choosing a Compute Engine
If you want to choose a specific compute engine to run on, you can set the environment
variable `MODIN_ENGINE` and Modin will do computation with that engine:
```bash
export MODIN_ENGINE=ray # Modin will use Ray
export MODIN_ENGINE=dask # Modin will use Dask
export MODIN_ENGINE=unidist # Modin will use Unidist
```
If you want to choose the Unidist engine, you should set the additional environment
variable ``UNIDIST_BACKEND``, because currently Modin only supports Unidist on MPI:
```bash
export UNIDIST_BACKEND=mpi # Unidist will use MPI backend
```
This can also be done within a notebook/interpreter before you import Modin:
```python
import modin.config as modin_cfg
import unidist.config as unidist_cfg
modin_cfg.Engine.put("ray") # Modin will use Ray
modin_cfg.Engine.put("dask") # Modin will use Dask
modin_cfg.Engine.put('unidist') # Modin will use Unidist
unidist_cfg.Backend.put('mpi') # Unidist will use MPI backend
```
Check [this Modin docs section](https://modin.readthedocs.io/en/latest/development/using_hdk.html) for HDK engine setup.
_Note: You should not change the engine after your first operation with Modin as it will result in undefined behavior._
#### Which engine should I use?
On Linux, MacOS, and Windows you can install and use either Ray, Dask or Unidist. There is no knowledge required
to use either of these engines as Modin abstracts away all of the complexity, so feel
free to pick either!
On Linux you also can choose [HDK](https://modin.readthedocs.io/en/latest/development/using_hdk.html), which is an experimental
engine based on [HDK](https://github.com/intel-ai/hdk) and included in the
[Intel® Distribution of Modin](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/distribution-of-modin.html),
which is a part of [Intel® oneAPI AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html).
### Pandas API Coverage
| pandas Object | Modin's Ray Engine Coverage | Modin's Dask Engine Coverage | Modin's Unidist Engine Coverage |
|-------------------|:------------------------------------------------------------------------------------:|:---------------:|:---------------:|
| `pd.DataFrame` | 
| | |
| `pd.Series` | 
| |
| `pd.read_csv` | ✅ | ✅ | ✅ |
| `pd.read_table` | ✅ | ✅ | ✅ |
| `pd.read_parquet` | ✅ | ✅ | ✅ |
| `pd.read_sql` | ✅ | ✅ | ✅ |
| `pd.read_feather` | ✅ | ✅ | ✅ |
| `pd.read_excel` | ✅ | ✅ | ✅ |
| `pd.read_json` | [✳️](https://github.com/modin-project/modin/issues/554) | [✳️](https://github.com/modin-project/modin/issues/554) | [✳️](https://github.com/modin-project/modin/issues/554) |
| `pd.read_
The `modin.pandas` DataFrame is an extremely light-weight parallel DataFrame.
Modin transparently distributes the data and computation so that you can continue using the same pandas API
while working with more data faster. Because it is so light-weight,
Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.
In pandas, you are only able to use one core at a time when you are doing computation of
any kind. With Modin, you are able to use all of the CPU cores on your machine. Even with a
traditionally synchronous task like `read_csv`, we see large speedups by efficiently
distributing the work across your entire machine.
```python
import modin.pandas as pd
df = pd.read_csv("my_dataset.csv")
```
#### Modin can handle the datasets that pandas can't
Often data scientists have to switch between different tools
for operating on datasets of different sizes. Processing large dataframes with pandas
is slow, and pandas does not support working with dataframes that are too large to fit
into the available memory. As a result, pandas workflows that work well
for prototyping on a few MBs of data do not scale to tens or hundreds of GBs (depending on the size
of your machine). Modin supports operating on data that does not fit in memory, so that you can comfortably
work with hundreds of GBs without worrying about substantial slowdown or memory errors.
With [cluster](https://modin.readthedocs.io/en/latest/getting_started/using_modin/using_modin_cluster.html)
and [out of core](https://modin.readthedocs.io/en/latest/getting_started/why_modin/out_of_core.html)
support, Modin is a DataFrame library with both great single-node performance and high
scalability in a cluster.
#### Modin Architecture
We designed [Modin's architecture](https://modin.readthedocs.io/en/latest/development/architecture.html)
to be modular so we can plug in different components as they develop and improve:
### Other Resources
#### Getting Started with Modin
- [Documentation](https://modin.readthedocs.io/en/latest/)
- [10-min Quickstart Guide](https://modin.readthedocs.io/en/latest/getting_started/quickstart.html)
- [Examples and Tutorials](https://modin.readthedocs.io/en/latest/getting_started/examples.html)
- [Videos and Blogposts](https://modin.readthedocs.io/en/latest/getting_started/examples.html#talks-podcasts)
- [Benchmarking Modin](https://modin.readthedocs.io/en/latest/usage_guide/benchmarking.html)
#### Modin Community
- [Slack](https://join.slack.com/t/modin-project/shared_invite/zt-yvk5hr3b-f08p_ulbuRWsAfg9rMY3uA)
- [Discourse](https://discuss.modin.org)
- [Twitter](https://twitter.com/modin_project)
- [Mailing List](https://groups.google.com/g/modin-dev)
- [GitHub Issues](https://github.com/modin-project/modin/issues)
- [StackOverflow](https://stackoverflow.com/questions/tagged/modin)
#### Learn More about Modin
- [Frequently Asked Questions (FAQs)](https://modin.readthedocs.io/en/latest/getting_started/faq.html)
- [Troubleshooting Guide](https://modin.readthedocs.io/en/latest/getting_started/troubleshooting.html)
- [Development Guide](https://modin.readthedocs.io/en/latest/development/index.html)
- Modin is built on many years of research and development at UC Berkeley. Check out these selected papers to learn more about how Modin works:
- [Flexible Rule-Based Decomposition and Metadata Independence in Modin](https://people.eecs.berkeley.edu/~totemtang/paper/Modin.pdf) (VLDB 2021)
- [Dataframe Systems: Theory, Architecture, and Implementation](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-193.pdf) (PhD Dissertation 2021)
- [Towards Scalable Dataframe Systems](https://arxiv.org/pdf/2001.00888.pdf) (VLDB 2020)
#### Getting Involved
***`modin.pandas` is currently under active development. Requests and contributions are welcome!***
For more information on how to contribute to Modin, check out the
[Modin Contribution Guide](https://modin.readthedocs.io/en/latest/development/contributing.html).
### License
[Apache License 2.0](LICENSE)
%package -n python3-modin
Summary: Modin: Make your pandas code run faster by changing one line of code.
Provides: python-modin
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-modin
|
In the GIFs below, Modin (left) and pandas (right) perform *the same pandas operations* on a 2GB dataset. The only difference between the two notebook examples is the import statement.
|
|
|---|---|
|
|
### Installation
#### From PyPI
Modin can be installed with `pip` on Linux, Windows and MacOS:
```bash
pip install modin[all] # (Recommended) Install Modin with all of Modin's currently supported engines.
```
If you want to install Modin with a specific engine, we recommend:
```bash
pip install modin[ray] # Install Modin dependencies and Ray.
pip install modin[dask] # Install Modin dependencies and Dask.
pip install modin[unidist] # Install Modin dependencies and Unidist to run on Unidist
```
Modin automatically detects which engine(s) you have installed and uses that for scheduling computation.
#### From conda-forge
Installing from [conda forge](https://github.com/conda-forge/modin-feedstock) using `modin-all`
will install Modin and four engines: [Ray](https://github.com/ray-project/ray), [Dask](https://github.com/dask/dask),
[Unidist](https://github.com/modin-project/unidist) and [HDK](https://github.com/intel-ai/hdk).
```bash
conda install -c conda-forge modin-all
```
Each engine can also be installed individually (and also as a combination of several engines):
```bash
conda install -c conda-forge modin-ray # Install Modin dependencies and Ray.
conda install -c conda-forge modin-dask # Install Modin dependencies and Dask.
conda install -c conda-forge modin-unidist # Install Modin dependencies and Unidist.
conda install -c conda-forge modin-hdk # Install Modin dependencies and HDK.
```
To speed up conda installation we recommend using libmamba solver. To do this install it in a base environment:
```bash
conda install -n base conda-libmamba-solver
```
and then use it during istallation either like:
```bash
conda install -c conda-forge modin-ray modin-hdk --experimental-solver=libmamba
```
or starting from conda 22.11 and libmamba solver 22.12 versions:
```bash
conda install -c conda-forge modin-ray modin-hdk --solver=libmamba
```
#### Choosing a Compute Engine
If you want to choose a specific compute engine to run on, you can set the environment
variable `MODIN_ENGINE` and Modin will do computation with that engine:
```bash
export MODIN_ENGINE=ray # Modin will use Ray
export MODIN_ENGINE=dask # Modin will use Dask
export MODIN_ENGINE=unidist # Modin will use Unidist
```
If you want to choose the Unidist engine, you should set the additional environment
variable ``UNIDIST_BACKEND``, because currently Modin only supports Unidist on MPI:
```bash
export UNIDIST_BACKEND=mpi # Unidist will use MPI backend
```
This can also be done within a notebook/interpreter before you import Modin:
```python
import modin.config as modin_cfg
import unidist.config as unidist_cfg
modin_cfg.Engine.put("ray") # Modin will use Ray
modin_cfg.Engine.put("dask") # Modin will use Dask
modin_cfg.Engine.put('unidist') # Modin will use Unidist
unidist_cfg.Backend.put('mpi') # Unidist will use MPI backend
```
Check [this Modin docs section](https://modin.readthedocs.io/en/latest/development/using_hdk.html) for HDK engine setup.
_Note: You should not change the engine after your first operation with Modin as it will result in undefined behavior._
#### Which engine should I use?
On Linux, MacOS, and Windows you can install and use either Ray, Dask or Unidist. There is no knowledge required
to use either of these engines as Modin abstracts away all of the complexity, so feel
free to pick either!
On Linux you also can choose [HDK](https://modin.readthedocs.io/en/latest/development/using_hdk.html), which is an experimental
engine based on [HDK](https://github.com/intel-ai/hdk) and included in the
[Intel® Distribution of Modin](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/distribution-of-modin.html),
which is a part of [Intel® oneAPI AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html).
### Pandas API Coverage
| pandas Object | Modin's Ray Engine Coverage | Modin's Dask Engine Coverage | Modin's Unidist Engine Coverage |
|-------------------|:------------------------------------------------------------------------------------:|:---------------:|:---------------:|
| `pd.DataFrame` | | | |
| `pd.Series` | | |
| `pd.read_csv` | ✅ | ✅ | ✅ |
| `pd.read_table` | ✅ | ✅ | ✅ |
| `pd.read_parquet` | ✅ | ✅ | ✅ |
| `pd.read_sql` | ✅ | ✅ | ✅ |
| `pd.read_feather` | ✅ | ✅ | ✅ |
| `pd.read_excel` | ✅ | ✅ | ✅ |
| `pd.read_json` | [✳️](https://github.com/modin-project/modin/issues/554) | [✳️](https://github.com/modin-project/modin/issues/554) | [✳️](https://github.com/modin-project/modin/issues/554) |
| `pd.read_
The `modin.pandas` DataFrame is an extremely light-weight parallel DataFrame.
Modin transparently distributes the data and computation so that you can continue using the same pandas API
while working with more data faster. Because it is so light-weight,
Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.
In pandas, you are only able to use one core at a time when you are doing computation of
any kind. With Modin, you are able to use all of the CPU cores on your machine. Even with a
traditionally synchronous task like `read_csv`, we see large speedups by efficiently
distributing the work across your entire machine.
```python
import modin.pandas as pd
df = pd.read_csv("my_dataset.csv")
```
#### Modin can handle the datasets that pandas can't
Often data scientists have to switch between different tools
for operating on datasets of different sizes. Processing large dataframes with pandas
is slow, and pandas does not support working with dataframes that are too large to fit
into the available memory. As a result, pandas workflows that work well
for prototyping on a few MBs of data do not scale to tens or hundreds of GBs (depending on the size
of your machine). Modin supports operating on data that does not fit in memory, so that you can comfortably
work with hundreds of GBs without worrying about substantial slowdown or memory errors.
With [cluster](https://modin.readthedocs.io/en/latest/getting_started/using_modin/using_modin_cluster.html)
and [out of core](https://modin.readthedocs.io/en/latest/getting_started/why_modin/out_of_core.html)
support, Modin is a DataFrame library with both great single-node performance and high
scalability in a cluster.
#### Modin Architecture
We designed [Modin's architecture](https://modin.readthedocs.io/en/latest/development/architecture.html)
to be modular so we can plug in different components as they develop and improve:
### Other Resources
#### Getting Started with Modin
- [Documentation](https://modin.readthedocs.io/en/latest/)
- [10-min Quickstart Guide](https://modin.readthedocs.io/en/latest/getting_started/quickstart.html)
- [Examples and Tutorials](https://modin.readthedocs.io/en/latest/getting_started/examples.html)
- [Videos and Blogposts](https://modin.readthedocs.io/en/latest/getting_started/examples.html#talks-podcasts)
- [Benchmarking Modin](https://modin.readthedocs.io/en/latest/usage_guide/benchmarking.html)
#### Modin Community
- [Slack](https://join.slack.com/t/modin-project/shared_invite/zt-yvk5hr3b-f08p_ulbuRWsAfg9rMY3uA)
- [Discourse](https://discuss.modin.org)
- [Twitter](https://twitter.com/modin_project)
- [Mailing List](https://groups.google.com/g/modin-dev)
- [GitHub Issues](https://github.com/modin-project/modin/issues)
- [StackOverflow](https://stackoverflow.com/questions/tagged/modin)
#### Learn More about Modin
- [Frequently Asked Questions (FAQs)](https://modin.readthedocs.io/en/latest/getting_started/faq.html)
- [Troubleshooting Guide](https://modin.readthedocs.io/en/latest/getting_started/troubleshooting.html)
- [Development Guide](https://modin.readthedocs.io/en/latest/development/index.html)
- Modin is built on many years of research and development at UC Berkeley. Check out these selected papers to learn more about how Modin works:
- [Flexible Rule-Based Decomposition and Metadata Independence in Modin](https://people.eecs.berkeley.edu/~totemtang/paper/Modin.pdf) (VLDB 2021)
- [Dataframe Systems: Theory, Architecture, and Implementation](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-193.pdf) (PhD Dissertation 2021)
- [Towards Scalable Dataframe Systems](https://arxiv.org/pdf/2001.00888.pdf) (VLDB 2020)
#### Getting Involved
***`modin.pandas` is currently under active development. Requests and contributions are welcome!***
For more information on how to contribute to Modin, check out the
[Modin Contribution Guide](https://modin.readthedocs.io/en/latest/development/contributing.html).
### License
[Apache License 2.0](LICENSE)
%package help
Summary: Development documents and examples for modin
Provides: python3-modin-doc
%description help
|
In the GIFs below, Modin (left) and pandas (right) perform *the same pandas operations* on a 2GB dataset. The only difference between the two notebook examples is the import statement.
|
|
|---|---|
|
|
### Installation
#### From PyPI
Modin can be installed with `pip` on Linux, Windows and MacOS:
```bash
pip install modin[all] # (Recommended) Install Modin with all of Modin's currently supported engines.
```
If you want to install Modin with a specific engine, we recommend:
```bash
pip install modin[ray] # Install Modin dependencies and Ray.
pip install modin[dask] # Install Modin dependencies and Dask.
pip install modin[unidist] # Install Modin dependencies and Unidist to run on Unidist
```
Modin automatically detects which engine(s) you have installed and uses that for scheduling computation.
#### From conda-forge
Installing from [conda forge](https://github.com/conda-forge/modin-feedstock) using `modin-all`
will install Modin and four engines: [Ray](https://github.com/ray-project/ray), [Dask](https://github.com/dask/dask),
[Unidist](https://github.com/modin-project/unidist) and [HDK](https://github.com/intel-ai/hdk).
```bash
conda install -c conda-forge modin-all
```
Each engine can also be installed individually (and also as a combination of several engines):
```bash
conda install -c conda-forge modin-ray # Install Modin dependencies and Ray.
conda install -c conda-forge modin-dask # Install Modin dependencies and Dask.
conda install -c conda-forge modin-unidist # Install Modin dependencies and Unidist.
conda install -c conda-forge modin-hdk # Install Modin dependencies and HDK.
```
To speed up conda installation we recommend using libmamba solver. To do this install it in a base environment:
```bash
conda install -n base conda-libmamba-solver
```
and then use it during istallation either like:
```bash
conda install -c conda-forge modin-ray modin-hdk --experimental-solver=libmamba
```
or starting from conda 22.11 and libmamba solver 22.12 versions:
```bash
conda install -c conda-forge modin-ray modin-hdk --solver=libmamba
```
#### Choosing a Compute Engine
If you want to choose a specific compute engine to run on, you can set the environment
variable `MODIN_ENGINE` and Modin will do computation with that engine:
```bash
export MODIN_ENGINE=ray # Modin will use Ray
export MODIN_ENGINE=dask # Modin will use Dask
export MODIN_ENGINE=unidist # Modin will use Unidist
```
If you want to choose the Unidist engine, you should set the additional environment
variable ``UNIDIST_BACKEND``, because currently Modin only supports Unidist on MPI:
```bash
export UNIDIST_BACKEND=mpi # Unidist will use MPI backend
```
This can also be done within a notebook/interpreter before you import Modin:
```python
import modin.config as modin_cfg
import unidist.config as unidist_cfg
modin_cfg.Engine.put("ray") # Modin will use Ray
modin_cfg.Engine.put("dask") # Modin will use Dask
modin_cfg.Engine.put('unidist') # Modin will use Unidist
unidist_cfg.Backend.put('mpi') # Unidist will use MPI backend
```
Check [this Modin docs section](https://modin.readthedocs.io/en/latest/development/using_hdk.html) for HDK engine setup.
_Note: You should not change the engine after your first operation with Modin as it will result in undefined behavior._
#### Which engine should I use?
On Linux, MacOS, and Windows you can install and use either Ray, Dask or Unidist. There is no knowledge required
to use either of these engines as Modin abstracts away all of the complexity, so feel
free to pick either!
On Linux you also can choose [HDK](https://modin.readthedocs.io/en/latest/development/using_hdk.html), which is an experimental
engine based on [HDK](https://github.com/intel-ai/hdk) and included in the
[Intel® Distribution of Modin](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/distribution-of-modin.html),
which is a part of [Intel® oneAPI AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html).
### Pandas API Coverage
| pandas Object | Modin's Ray Engine Coverage | Modin's Dask Engine Coverage | Modin's Unidist Engine Coverage |
|-------------------|:------------------------------------------------------------------------------------:|:---------------:|:---------------:|
| `pd.DataFrame` | | | |
| `pd.Series` | | |
| `pd.read_csv` | ✅ | ✅ | ✅ |
| `pd.read_table` | ✅ | ✅ | ✅ |
| `pd.read_parquet` | ✅ | ✅ | ✅ |
| `pd.read_sql` | ✅ | ✅ | ✅ |
| `pd.read_feather` | ✅ | ✅ | ✅ |
| `pd.read_excel` | ✅ | ✅ | ✅ |
| `pd.read_json` | [✳️](https://github.com/modin-project/modin/issues/554) | [✳️](https://github.com/modin-project/modin/issues/554) | [✳️](https://github.com/modin-project/modin/issues/554) |
| `pd.read_
The `modin.pandas` DataFrame is an extremely light-weight parallel DataFrame.
Modin transparently distributes the data and computation so that you can continue using the same pandas API
while working with more data faster. Because it is so light-weight,
Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.
In pandas, you are only able to use one core at a time when you are doing computation of
any kind. With Modin, you are able to use all of the CPU cores on your machine. Even with a
traditionally synchronous task like `read_csv`, we see large speedups by efficiently
distributing the work across your entire machine.
```python
import modin.pandas as pd
df = pd.read_csv("my_dataset.csv")
```
#### Modin can handle the datasets that pandas can't
Often data scientists have to switch between different tools
for operating on datasets of different sizes. Processing large dataframes with pandas
is slow, and pandas does not support working with dataframes that are too large to fit
into the available memory. As a result, pandas workflows that work well
for prototyping on a few MBs of data do not scale to tens or hundreds of GBs (depending on the size
of your machine). Modin supports operating on data that does not fit in memory, so that you can comfortably
work with hundreds of GBs without worrying about substantial slowdown or memory errors.
With [cluster](https://modin.readthedocs.io/en/latest/getting_started/using_modin/using_modin_cluster.html)
and [out of core](https://modin.readthedocs.io/en/latest/getting_started/why_modin/out_of_core.html)
support, Modin is a DataFrame library with both great single-node performance and high
scalability in a cluster.
#### Modin Architecture
We designed [Modin's architecture](https://modin.readthedocs.io/en/latest/development/architecture.html)
to be modular so we can plug in different components as they develop and improve:
### Other Resources
#### Getting Started with Modin
- [Documentation](https://modin.readthedocs.io/en/latest/)
- [10-min Quickstart Guide](https://modin.readthedocs.io/en/latest/getting_started/quickstart.html)
- [Examples and Tutorials](https://modin.readthedocs.io/en/latest/getting_started/examples.html)
- [Videos and Blogposts](https://modin.readthedocs.io/en/latest/getting_started/examples.html#talks-podcasts)
- [Benchmarking Modin](https://modin.readthedocs.io/en/latest/usage_guide/benchmarking.html)
#### Modin Community
- [Slack](https://join.slack.com/t/modin-project/shared_invite/zt-yvk5hr3b-f08p_ulbuRWsAfg9rMY3uA)
- [Discourse](https://discuss.modin.org)
- [Twitter](https://twitter.com/modin_project)
- [Mailing List](https://groups.google.com/g/modin-dev)
- [GitHub Issues](https://github.com/modin-project/modin/issues)
- [StackOverflow](https://stackoverflow.com/questions/tagged/modin)
#### Learn More about Modin
- [Frequently Asked Questions (FAQs)](https://modin.readthedocs.io/en/latest/getting_started/faq.html)
- [Troubleshooting Guide](https://modin.readthedocs.io/en/latest/getting_started/troubleshooting.html)
- [Development Guide](https://modin.readthedocs.io/en/latest/development/index.html)
- Modin is built on many years of research and development at UC Berkeley. Check out these selected papers to learn more about how Modin works:
- [Flexible Rule-Based Decomposition and Metadata Independence in Modin](https://people.eecs.berkeley.edu/~totemtang/paper/Modin.pdf) (VLDB 2021)
- [Dataframe Systems: Theory, Architecture, and Implementation](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-193.pdf) (PhD Dissertation 2021)
- [Towards Scalable Dataframe Systems](https://arxiv.org/pdf/2001.00888.pdf) (VLDB 2020)
#### Getting Involved
***`modin.pandas` is currently under active development. Requests and contributions are welcome!***
For more information on how to contribute to Modin, check out the
[Modin Contribution Guide](https://modin.readthedocs.io/en/latest/development/contributing.html).
### License
[Apache License 2.0](LICENSE)
%prep
%autosetup -n modin-0.19.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-modin -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot